ArticleBMC medical imaging2023
Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 8 citations in OpenAlex.
- Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.Tomography (Ann Arbor, Mich.) · 2026Article
- Multimodal combined model integrating 2.5D deep learning and habitat radiomics for malignancy discrimination in ≤2 cm BI-RADS 4 breast lesions.Quantitative imaging in medicine and surgery · 2026Article
- Breathprints for Breast Cancer: Evaluating a Non-Invasive Approach to BI-RADS 4 Risk Stratification in a Preliminary Study.Cancers · 2026Article
- A dual-core driven hybrid radiomics integrates multiple views of greyscale ultrasound image for the early management from diagnosis to biopsy suggestion of BI-RADS 4 breast lesions: a prospective multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Value of deep learning model for predicting Breast Imaging Reporting and Data System 3 and 4A lesions on mammography.Quantitative imaging in medicine and surgery · 2025Article
- MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: a multicenter study.Insights into imaging · 2025Article
- Reducing unnecessary biopsies of BI-RADS 4 lesions based on a deep learning model for mammography.Frontiers in oncology · 2025Article
- The value of contrast-enhanced energy-spectrum mammography combined with clinical indicators in detecting breast cancer in Breast Imaging Reporting and Data System (BI-RADS) 4 lesions.Quantitative imaging in medicine and surgery · 2024Article
- Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.BMC medical imaging · 2024Article
- Can AI Reduce the Harms of Screening Mammography?Radiology. Artificial intelligence · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions.
methodsA total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2.
resultsThe S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110).
conclusionThe S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.
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